Anaerobic Digestion Model (AM2) for the Description of Biogas Processes at Dynamic Feedstock Loading Rates

Abstract

The anaerobic digestion of biomass is a complex, dynamic, and highly nonlinear process. Hence, a mathematical model that is capable of describing the complete operability region with sufficient accuracy cannot be identifiable even with the most advanced process monitoring technology. The complexity of the anaerobic digestion model no. 1 (ADM1) leads to the application of either parameter‐reduced versions of the ADM1 or simpler models as, e.g., the anaerobic digestion model AM2. A comparative study for the simulation of biogas formation at dynamic feedstock loading using maize silage was performed with both models.

A new and simple kinetic model for assessing the dynamic behavior and simulating the biochemical methane potential (BMP) of sewage sludge in the presence of fly ash

“…Parameters related to methanogenic activity ( μ max, 2 and K S , 2 ) reported herein also agreed with values reported in the literature, although the values were somewhat lower than the reference values and closer to those reported by Fitamo et al . Regarding the value of μ max, 1 related to acidogenesis, it was lower herein than those reported by other researchers, and were closer than those reported by Fitamo et al …”

“…This means also that the consumption of S 1 ( K 1 ) and S 2 ( K 2 ) must be higher than those reported in other works; nevertheless, the K 2 : K 1 ratio is within the standard deviation (SD) presented by other research work . The same situation is observed for the stoichiometric parameters K 3 and K 6 , which are higher than the reference values or other values reported in the literature for other kinds of substrates, especially K 6 , although the ratio K 6 : K 3 also is within the SD reported by Bernard et al . Therefore, the stoichiometric ratio K 6 : K 3 is within the range of values reported in the literature.…”

Application of monod two-substrate kinetics with an intermediate for anaerobic co-digestion of distillery wastewater and molasses/glycerol waste in batch experiments

“…The model calibration algorithm diagram to identify variable parameters is illustrated in Figure 1. Firstly, preliminary model fitting was performed to set a default or constant values as follows: m mi ¼ 0:2m me , k de ¼ 0:15m me , k ds ¼ 0:1m me (decay rate of biomass was reported in a range from 5% to 15% of the maximum growth rate, Arzate et al 2017;Arzate 2019), f SsX ¼ 0.7, f isX ¼ 0.8, K Se ¼ K Si ¼ K Ss ¼ 10,000 mg/L (the half saturation constants were reported in a range between 7,000 mg/L and 12,000 mg/L, Bernard et al 2001). The biomass yield coefficients Y XeSe ¼ Y XeSi ¼ Y XsSs ¼ 0.13 mL/(mg/L) were fixed for all cases, X 0 ¼ 8,000 mg COD was estimated by using the available value 2.67 g COD/g VSS of inoculum obtained from a pond of palm oil mill effluent (Madaki & Seng 2013).…”

Prediction of the Methane Production in Biogas Plants Using a Combined Gompertz and Machine Learning Model

“…In order to develop an ADM2 it is necessary to have a uniform approach to the mechanisms and challenges which will require further research. After 2015, other models and optimizations of the ADM1 have been performed [ 8 ].…”